paperwithcode-mcp

MCP server that brings AI paper reading and code repository discovery from Hugging Face Papers into any MCP-compatible client (Claude Desktop, IDE plugins, etc.). Supports both stdio and SSE transports.

Overview

Keeping up with AI research means reading papers, finding code implementations, and tracking daily new releases. This server bridges Hugging Face Papers' rich metadata — AI summaries, GitHub star counts, full paper markdown, and daily trending lists — directly into your AI assistant's toolset. Instead of switching between browser tabs, you query papers conversationally.

What you can do:

• Paste an arXiv ID and get the corresponding GitHub repo (with star count)

• Ask for a paper's details: title, authors, abstract, AI summary, keywords

• Read a paper's full text as markdown in your conversation

• List today's trending papers on Hugging Face Papers

Related MCP server: paper-fetch-mcp

Quick Start

pip install git+https://github.com/GtJerry111/paperwithcode-hf-mcp.git
paperwithcode-mcp

The server starts in stdio mode, ready to connect to Claude Desktop or any MCP host. Add it to your claude_desktop_config.json (see Claude Desktop Integration) and you're done.

Tools

Usage Examples

Each tool accepts simple string arguments and returns JSON. Here is what you can expect from each:

resolve_code_link — "2508.02739" returns {"github_url": "https://github.com/shiyu-coder/Kronos"}

get_paper_details — "2508.02739" returns a rich object with title, authors (list), summary, upvotes (integer), githubStars (integer), ai_summary (string), ai_keywords (list), and more.

read_paper — "2508.02739" returns the full paper as a markdown string (all sections: abstract, introduction, method, results, etc.).

list_daily_papers — "2026-06-23" returns up to 50 papers with id, title, authors, summary, upvotes, and comment count. Omit the date to get today's papers.

Deployment

pip

pip install git+https://github.com/GtJerry111/paperwithcode-hf-mcp.git

paperwithcode-mcp                                        # stdio (default)
paperwithcode-mcp --transport sse --host 0.0.0.0 --port 8787  # SSE

uv

uv tool install git+https://github.com/GtJerry111/paperwithcode-hf-mcp.git

paperwithcode-mcp                                        # stdio

# Update later
uv tool upgrade paperwithcode-mcp

Docker

docker build -t paperwithcode-mcp .
docker run -i --rm paperwithcode-mcp                     # stdio
docker run -i --rm -p 8787:8787 paperwithcode-mcp \
  --transport sse --host 0.0.0.0 --port 8787              # SSE

Claude Desktop Integration

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "paperwithcode": {
      "command": "paperwithcode-mcp",
      "args": []
    }
  }
}

If paperwithcode-mcp is not in your PATH after pip install, use the full Python module path or the uvx launcher:

{
  "mcpServers": {
    "paperwithcode": {
      "command": "uvx",
      "args": ["paperwithcode-mcp"]
    }
  }
}

Development

git clone https://github.com/GtJerry111/paperwithcode-hf-mcp.git
cd paperwithcode-hf-mcp

# pip
pip install -e ".[dev]"

# uv
uv sync --group dev

Architecture

The server has a simple data flow:

MCP tool call -> mcp_server.py (FastMCP) -> resolver.py (business logic)
    -> client.py (curl/network) + parser.py (HTML extraction)

• mcp_server.py — FastMCP instance with 4 tool definitions and the CLI entry point

• resolver.py — orchestrates calls between client and parser, returns typed results

• client.py — PaperPageClient wraps curl subprocess, handles proxy and retries

• parser.py — extracts structured data from Hugging Face paper pages

Data Sources

• https://huggingface.co/papers/{arxiv_id} — individual paper page (embedded JSON in data-props)

• https://huggingface.co/api/daily_papers?date=YYYY-MM-DD — daily papers API (no auth)

• markdownContentUrl — full paper text as markdown from the arXiv HTML conversion

Environment Variables

Limitations

This project uses Hugging Face Papers as its data source, NOT the paperswithcode.com API (which is no longer available). As a result:

• No paper search by keyword or title

• No conference, proceedings, or author browsing

• No benchmark results or dataset listings

License

MIT